Overview

Dataset statistics

Number of variables15
Number of observations119
Missing cells622
Missing cells (%)34.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory14.1 KiB
Average record size in memory121.1 B

Variable types

Numeric15

Warnings

Year is highly correlated with All natural disasters and 9 other fieldsHigh correlation
All natural disasters is highly correlated with Year and 9 other fieldsHigh correlation
Drought is highly correlated with Year and 5 other fieldsHigh correlation
Extreme temperature is highly correlated with Year and 6 other fieldsHigh correlation
Extreme weather is highly correlated with Year and 9 other fieldsHigh correlation
Flood is highly correlated with Year and 9 other fieldsHigh correlation
Landslide is highly correlated with Year and 8 other fieldsHigh correlation
Wildfire is highly correlated with Year and 5 other fieldsHigh correlation
TotalCost: All natural disasters is highly correlated with Year and 7 other fieldsHigh correlation
TotalCost: Extreme weather is highly correlated with Year and 5 other fieldsHigh correlation
TotalCost: Flood is highly correlated with Year and 5 other fieldsHigh correlation
Year is highly correlated with All natural disasters and 11 other fieldsHigh correlation
All natural disasters is highly correlated with Year and 10 other fieldsHigh correlation
Drought is highly correlated with Year and 10 other fieldsHigh correlation
Extreme temperature is highly correlated with Year and 10 other fieldsHigh correlation
Extreme weather is highly correlated with Year and 10 other fieldsHigh correlation
Flood is highly correlated with Year and 10 other fieldsHigh correlation
Landslide is highly correlated with Year and 9 other fieldsHigh correlation
Wildfire is highly correlated with Year and 10 other fieldsHigh correlation
TotalCost: All natural disasters is highly correlated with Year and 10 other fieldsHigh correlation
TotalCost: Drought is highly correlated with Year and 1 other fieldsHigh correlation
TotalCost: Extreme weather is highly correlated with Year and 10 other fieldsHigh correlation
TotalCost: Flood is highly correlated with Year and 10 other fieldsHigh correlation
TotalCost: Wildfire is highly correlated with Year and 8 other fieldsHigh correlation
Year is highly correlated with All natural disasters and 9 other fieldsHigh correlation
All natural disasters is highly correlated with Year and 9 other fieldsHigh correlation
Drought is highly correlated with Year and 5 other fieldsHigh correlation
Extreme temperature is highly correlated with Year and 7 other fieldsHigh correlation
Extreme weather is highly correlated with Year and 9 other fieldsHigh correlation
Flood is highly correlated with Year and 9 other fieldsHigh correlation
Landslide is highly correlated with Year and 8 other fieldsHigh correlation
Wildfire is highly correlated with Year and 6 other fieldsHigh correlation
TotalCost: All natural disasters is highly correlated with Year and 9 other fieldsHigh correlation
TotalCost: Extreme weather is highly correlated with Year and 9 other fieldsHigh correlation
TotalCost: Flood is highly correlated with Year and 7 other fieldsHigh correlation
TotalCost: Wildfire is highly correlated with TotalCost: LandslideHigh correlation
Wildfire is highly correlated with Landslide and 8 other fieldsHigh correlation
Year is highly correlated with Landslide and 4 other fieldsHigh correlation
Landslide is highly correlated with Wildfire and 9 other fieldsHigh correlation
TotalCost: Drought is highly correlated with Wildfire and 5 other fieldsHigh correlation
Flood is highly correlated with Wildfire and 9 other fieldsHigh correlation
TotalCost: Extreme weather is highly correlated with Landslide and 6 other fieldsHigh correlation
TotalCost: Flood is highly correlated with Wildfire and 10 other fieldsHigh correlation
Drought is highly correlated with Wildfire and 6 other fieldsHigh correlation
All natural disasters is highly correlated with Wildfire and 9 other fieldsHigh correlation
TotalCost: All natural disasters is highly correlated with Wildfire and 9 other fieldsHigh correlation
Extreme temperature is highly correlated with Wildfire and 7 other fieldsHigh correlation
TotalCost: Landslide is highly correlated with TotalCost: Wildfire and 2 other fieldsHigh correlation
Extreme weather is highly correlated with Wildfire and 8 other fieldsHigh correlation
Drought has 46 (38.7%) missing values Missing
Extreme temperature has 62 (52.1%) missing values Missing
Extreme weather has 4 (3.4%) missing values Missing
Flood has 24 (20.2%) missing values Missing
Landslide has 39 (32.8%) missing values Missing
Wildfire has 57 (47.9%) missing values Missing
TotalCost: Drought has 70 (58.8%) missing values Missing
TotalCost: Extreme temperature has 85 (71.4%) missing values Missing
TotalCost: Extreme weather has 25 (21.0%) missing values Missing
TotalCost: Flood has 50 (42.0%) missing values Missing
TotalCost: Landslide has 85 (71.4%) missing values Missing
TotalCost: Wildfire has 75 (63.0%) missing values Missing
Year is uniformly distributed Uniform
Year has unique values Unique
TotalCost: All natural disasters has 10 (8.4%) zeros Zeros
TotalCost: Extreme temperature has 2 (1.7%) zeros Zeros

Reproduction

Analysis started2021-10-31 14:34:44.984253
Analysis finished2021-10-31 14:35:12.803364
Duration27.82 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

Year
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct119
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1959
Minimum1900
Maximum2018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2021-10-31T10:35:12.908533image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1900
5-th percentile1905.9
Q11929.5
median1959
Q31988.5
95-th percentile2012.1
Maximum2018
Range118
Interquartile range (IQR)59

Descriptive statistics

Standard deviation34.49637662
Coefficient of variation (CV)0.01760917643
Kurtosis-1.2
Mean1959
Median Absolute Deviation (MAD)30
Skewness0
Sum233121
Variance1190
MonotonicityStrictly increasing
2021-10-31T10:35:13.062996image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20181
 
0.8%
19431
 
0.8%
19311
 
0.8%
19321
 
0.8%
19331
 
0.8%
19341
 
0.8%
19351
 
0.8%
19361
 
0.8%
19371
 
0.8%
19381
 
0.8%
Other values (109)109
91.6%
ValueCountFrequency (%)
19001
0.8%
19011
0.8%
19021
0.8%
19031
0.8%
19041
0.8%
19051
0.8%
19061
0.8%
19071
0.8%
19081
0.8%
19091
0.8%
ValueCountFrequency (%)
20181
0.8%
20171
0.8%
20161
0.8%
20151
0.8%
20141
0.8%
20131
0.8%
20121
0.8%
20111
0.8%
20101
0.8%
20091
0.8%

All natural disasters
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct76
Distinct (%)63.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean111.0252101
Minimum2
Maximum432
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2021-10-31T10:35:13.224287image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4
Q110.5
median33
Q3202
95-th percentile381.3
Maximum432
Range430
Interquartile range (IQR)191.5

Descriptive statistics

Standard deviation133.9582823
Coefficient of variation (CV)1.206557341
Kurtosis-0.3177973197
Mean111.0252101
Median Absolute Deviation (MAD)28
Skewness1.074830046
Sum13212
Variance17944.82139
MonotonicityNot monotonic
2021-10-31T10:35:13.372967image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127
 
5.9%
76
 
5.0%
55
 
4.2%
65
 
4.2%
24
 
3.4%
44
 
3.4%
114
 
3.4%
93
 
2.5%
173
 
2.5%
153
 
2.5%
Other values (66)75
63.0%
ValueCountFrequency (%)
24
3.4%
44
3.4%
55
4.2%
65
4.2%
76
5.0%
82
 
1.7%
93
2.5%
101
 
0.8%
114
3.4%
127
5.9%
ValueCountFrequency (%)
4321
0.8%
4211
0.8%
4141
0.8%
4111
0.8%
4011
0.8%
3931
0.8%
3801
0.8%
3761
0.8%
3601
0.8%
3521
0.8%

Drought
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct26
Distinct (%)35.6%
Missing46
Missing (%)38.7%
Infinite0
Infinite (%)0.0%
Mean9.95890411
Minimum1
Maximum32
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2021-10-31T10:35:13.512080image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median9
Q315
95-th percentile23.8
Maximum32
Range31
Interquartile range (IQR)13

Descriptive statistics

Standard deviation7.727005953
Coefficient of variation (CV)0.775889181
Kurtosis-0.1182686035
Mean9.95890411
Median Absolute Deviation (MAD)7
Skewness0.6899044212
Sum727
Variance59.706621
MonotonicityNot monotonic
2021-10-31T10:35:13.636983image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
115
 
12.6%
97
 
5.9%
77
 
5.9%
24
 
3.4%
184
 
3.4%
134
 
3.4%
63
 
2.5%
113
 
2.5%
173
 
2.5%
152
 
1.7%
Other values (16)21
17.6%
(Missing)46
38.7%
ValueCountFrequency (%)
115
12.6%
24
 
3.4%
31
 
0.8%
42
 
1.7%
51
 
0.8%
63
 
2.5%
77
5.9%
81
 
0.8%
97
5.9%
101
 
0.8%
ValueCountFrequency (%)
321
 
0.8%
281
 
0.8%
271
 
0.8%
251
 
0.8%
231
 
0.8%
221
 
0.8%
211
 
0.8%
202
1.7%
184
3.4%
173
2.5%

Extreme temperature
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct24
Distinct (%)42.1%
Missing62
Missing (%)52.1%
Infinite0
Infinite (%)0.0%
Mean10.0877193
Minimum1
Maximum51
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2021-10-31T10:35:13.759446image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median6
Q314
95-th percentile29.2
Maximum51
Range50
Interquartile range (IQR)12

Descriptive statistics

Standard deviation10.40137474
Coefficient of variation (CV)1.0310928
Kurtosis3.050678455
Mean10.0877193
Median Absolute Deviation (MAD)5
Skewness1.602279903
Sum575
Variance108.1885965
MonotonicityNot monotonic
2021-10-31T10:35:13.880932image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
210
 
8.4%
18
 
6.7%
35
 
4.2%
254
 
3.4%
83
 
2.5%
133
 
2.5%
123
 
2.5%
42
 
1.7%
62
 
1.7%
162
 
1.7%
Other values (14)15
 
12.6%
(Missing)62
52.1%
ValueCountFrequency (%)
18
6.7%
210
8.4%
35
4.2%
42
 
1.7%
52
 
1.7%
62
 
1.7%
71
 
0.8%
83
 
2.5%
91
 
0.8%
101
 
0.8%
ValueCountFrequency (%)
511
 
0.8%
311
 
0.8%
301
 
0.8%
291
 
0.8%
254
3.4%
241
 
0.8%
231
 
0.8%
171
 
0.8%
162
1.7%
151
 
0.8%

Extreme weather
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct58
Distinct (%)50.4%
Missing4
Missing (%)3.4%
Infinite0
Infinite (%)0.0%
Mean35.90434783
Minimum1
Maximum137
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2021-10-31T10:35:14.013042image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median19
Q369.5
95-th percentile108.9
Maximum137
Range136
Interquartile range (IQR)65.5

Descriptive statistics

Standard deviation39.39720667
Coefficient of variation (CV)1.097282336
Kurtosis-0.4979364287
Mean35.90434783
Median Absolute Deviation (MAD)17
Skewness0.9392693519
Sum4129
Variance1552.139893
MonotonicityNot monotonic
2021-10-31T10:35:14.163059image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
211
 
9.2%
110
 
8.4%
37
 
5.9%
56
 
5.0%
76
 
5.0%
44
 
3.4%
843
 
2.5%
253
 
2.5%
103
 
2.5%
283
 
2.5%
Other values (48)59
49.6%
(Missing)4
 
3.4%
ValueCountFrequency (%)
110
8.4%
211
9.2%
37
5.9%
44
 
3.4%
56
5.0%
61
 
0.8%
76
5.0%
81
 
0.8%
91
 
0.8%
103
 
2.5%
ValueCountFrequency (%)
1371
 
0.8%
1301
 
0.8%
1241
 
0.8%
1221
 
0.8%
1181
 
0.8%
1111
 
0.8%
1081
 
0.8%
1061
 
0.8%
1053
2.5%
1021
 
0.8%

Flood
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct59
Distinct (%)62.1%
Missing24
Missing (%)20.2%
Infinite0
Infinite (%)0.0%
Mean52.21052632
Minimum1
Maximum226
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2021-10-31T10:35:14.326144image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12.5
median20
Q390
95-th percentile167.1
Maximum226
Range225
Interquartile range (IQR)87.5

Descriptive statistics

Standard deviation61.82953483
Coefficient of variation (CV)1.184235042
Kurtosis0.08698405192
Mean52.21052632
Median Absolute Deviation (MAD)19
Skewness1.137268139
Sum4960
Variance3822.891377
MonotonicityNot monotonic
2021-10-31T10:35:14.467525image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
114
 
11.8%
210
 
8.4%
83
 
2.5%
472
 
1.7%
482
 
1.7%
172
 
1.7%
152
 
1.7%
942
 
1.7%
202
 
1.7%
1572
 
1.7%
Other values (49)54
45.4%
(Missing)24
20.2%
ValueCountFrequency (%)
114
11.8%
210
8.4%
32
 
1.7%
42
 
1.7%
51
 
0.8%
72
 
1.7%
83
 
2.5%
92
 
1.7%
101
 
0.8%
111
 
0.8%
ValueCountFrequency (%)
2261
0.8%
2181
0.8%
1931
0.8%
1841
0.8%
1721
0.8%
1651
0.8%
1611
0.8%
1601
0.8%
1581
0.8%
1572
1.7%

Landslide
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct25
Distinct (%)31.2%
Missing39
Missing (%)32.8%
Infinite0
Infinite (%)0.0%
Mean8.9
Minimum1
Maximum32
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2021-10-31T10:35:14.596307image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median6
Q313.5
95-th percentile24.05
Maximum32
Range31
Interquartile range (IQR)11.5

Descriptive statistics

Standard deviation8.121856744
Coefficient of variation (CV)0.9125681734
Kurtosis0.01013716507
Mean8.9
Median Absolute Deviation (MAD)5
Skewness0.9471468592
Sum712
Variance65.96455696
MonotonicityNot monotonic
2021-10-31T10:35:14.703182image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
118
15.1%
29
 
7.6%
57
 
5.9%
136
 
5.0%
155
 
4.2%
44
 
3.4%
203
 
2.5%
63
 
2.5%
113
 
2.5%
82
 
1.7%
Other values (15)20
16.8%
(Missing)39
32.8%
ValueCountFrequency (%)
118
15.1%
29
7.6%
31
 
0.8%
44
 
3.4%
57
 
5.9%
63
 
2.5%
72
 
1.7%
82
 
1.7%
92
 
1.7%
102
 
1.7%
ValueCountFrequency (%)
321
 
0.8%
291
 
0.8%
281
 
0.8%
251
 
0.8%
242
1.7%
222
1.7%
211
 
0.8%
203
2.5%
181
 
0.8%
171
 
0.8%

Wildfire
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct17
Distinct (%)27.4%
Missing57
Missing (%)47.9%
Infinite0
Infinite (%)0.0%
Mean6.935483871
Minimum1
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2021-10-31T10:35:14.811444image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4.5
Q310
95-th percentile18
Maximum30
Range29
Interquartile range (IQR)8

Descriptive statistics

Standard deviation6.256461493
Coefficient of variation (CV)0.9020944479
Kurtosis2.216512222
Mean6.935483871
Median Absolute Deviation (MAD)3.5
Skewness1.435725094
Sum430
Variance39.14331042
MonotonicityNot monotonic
2021-10-31T10:35:14.923923image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
111
 
9.2%
38
 
6.7%
27
 
5.9%
85
 
4.2%
45
 
4.2%
134
 
3.4%
73
 
2.5%
103
 
2.5%
92
 
1.7%
52
 
1.7%
Other values (7)12
 
10.1%
(Missing)57
47.9%
ValueCountFrequency (%)
111
9.2%
27
5.9%
38
6.7%
45
4.2%
52
 
1.7%
62
 
1.7%
73
 
2.5%
85
4.2%
92
 
1.7%
103
 
2.5%
ValueCountFrequency (%)
301
 
0.8%
222
 
1.7%
182
 
1.7%
161
 
0.8%
142
 
1.7%
134
3.4%
122
 
1.7%
103
2.5%
92
 
1.7%
85
4.2%

TotalCost: All natural disasters
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct102
Distinct (%)85.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.703562965 × 1010
Minimum0
Maximum3.64093168 × 1011
Zeros10
Zeros (%)8.4%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2021-10-31T10:35:15.057808image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q160750000
median1043000000
Q32.72505895 × 1010
95-th percentile1.444758745 × 1011
Maximum3.64093168 × 1011
Range3.64093168 × 1011
Interquartile range (IQR)2.71898395 × 1010

Descriptive statistics

Standard deviation5.517178259 × 1010
Coefficient of variation (CV)2.040706405
Kurtosis12.64382666
Mean2.703562965 × 1010
Median Absolute Deviation (MAD)1043000000
Skewness3.127218879
Sum3.217239928 × 1012
Variance3.043925595 × 1021
MonotonicityNot monotonic
2021-10-31T10:35:15.208351image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
010
 
8.4%
200000003
 
2.5%
300000003
 
2.5%
80000002
 
1.7%
4800000002
 
1.7%
500000002
 
1.7%
250000002
 
1.7%
28688310001
 
0.8%
1.36378448 × 10111
 
0.8%
8.413104 × 10101
 
0.8%
Other values (92)92
77.3%
ValueCountFrequency (%)
010
8.4%
80000002
 
1.7%
200000003
 
2.5%
210000001
 
0.8%
250000002
 
1.7%
255000001
 
0.8%
280000001
 
0.8%
300000003
 
2.5%
350000001
 
0.8%
400000001
 
0.8%
ValueCountFrequency (%)
3.64093168 × 10111
0.8%
2.14205351 × 10111
0.8%
1.90849247 × 10111
0.8%
1.56692232 × 10111
0.8%
1.54967039 × 10111
0.8%
1.47781165 × 10111
0.8%
1.4410862 × 10111
0.8%
1.36378448 × 10111
0.8%
1.32194096 × 10111
0.8%
1.19484189 × 10111
0.8%

TotalCost: Drought
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct49
Distinct (%)100.0%
Missing70
Missing (%)58.8%
Infinite0
Infinite (%)0.0%
Mean3403305592
Minimum1000000
Maximum2.548 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2021-10-31T10:35:15.372531image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1000000
5-th percentile2820000
Q1234000000
median1515100000
Q33628700000
95-th percentile1.279912 × 1010
Maximum2.548 × 1010
Range2.5479 × 1010
Interquartile range (IQR)3394700000

Descriptive statistics

Standard deviation5138275225
Coefficient of variation (CV)1.509789552
Kurtosis8.001582077
Mean3403305592
Median Absolute Deviation (MAD)1451100000
Skewness2.661103529
Sum1.66761974 × 1011
Variance2.640187229 × 1019
MonotonicityNot monotonic
2021-10-31T10:35:15.524342image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
2.548 × 10101
 
0.8%
2000000001
 
0.8%
640000001
 
0.8%
38847000001
 
0.8%
1.39952 × 10101
 
0.8%
74600000001
 
0.8%
29918000001
 
0.8%
11139540001
 
0.8%
18012870001
 
0.8%
7060000001
 
0.8%
Other values (39)39
32.8%
(Missing)70
58.8%
ValueCountFrequency (%)
10000001
0.8%
20000001
0.8%
21000001
0.8%
39000001
0.8%
364000001
0.8%
640000001
0.8%
760000001
0.8%
1128000001
0.8%
1271180001
0.8%
1870000001
0.8%
ValueCountFrequency (%)
2.548 × 10101
0.8%
1.9812399 × 10101
0.8%
1.39952 × 10101
0.8%
1.1005 × 10101
0.8%
93470000001
0.8%
81420000001
0.8%
78714750001
0.8%
74600000001
0.8%
66017390001
0.8%
52536690001
0.8%

TotalCost: Extreme temperature
Real number (ℝ≥0)

MISSING
ZEROS

Distinct29
Distinct (%)85.3%
Missing85
Missing (%)71.4%
Infinite0
Infinite (%)0.0%
Mean1849010088
Minimum0
Maximum2.194 × 1010
Zeros2
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2021-10-31T10:35:15.701886image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile390000
Q1111000000
median560000000
Q31585750000
95-th percentile7160750000
Maximum2.194 × 1010
Range2.194 × 1010
Interquartile range (IQR)1474750000

Descriptive statistics

Standard deviation4196813939
Coefficient of variation (CV)2.269762597
Kurtosis17.33935077
Mean1849010088
Median Absolute Deviation (MAD)463000000
Skewness4.030583397
Sum6.2866343 × 1010
Variance1.761324724 × 1019
MonotonicityNot monotonic
2021-10-31T10:35:15.859287image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
10000000003
 
2.5%
02
 
1.7%
4000000002
 
1.7%
1000000002
 
1.7%
1528010001
 
0.8%
3701590001
 
0.8%
8343000001
 
0.8%
1.252 × 10101
 
0.8%
940000001
 
0.8%
892500001
 
0.8%
Other values (19)19
 
16.0%
(Missing)85
71.4%
ValueCountFrequency (%)
02
1.7%
6000001
0.8%
330000001
0.8%
800000001
0.8%
892500001
0.8%
940000001
0.8%
1000000002
1.7%
1440000001
0.8%
1528010001
0.8%
2001100001
0.8%
ValueCountFrequency (%)
2.194 × 10101
0.8%
1.252 × 10101
0.8%
42750000001
0.8%
30280000001
0.8%
28000000001
0.8%
25180000001
0.8%
20000000001
0.8%
17500000001
0.8%
17270000001
0.8%
11620000001
0.8%

TotalCost: Extreme weather
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct89
Distinct (%)94.7%
Missing25
Missing (%)21.0%
Infinite0
Infinite (%)0.0%
Mean1.363404069 × 1010
Minimum500000
Maximum1.84793461 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2021-10-31T10:35:16.034721image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum500000
5-th percentile15200000
Q173500000
median1365849000
Q31.4504626 × 1010
95-th percentile5.97674537 × 1010
Maximum1.84793461 × 1011
Range1.84792961 × 1011
Interquartile range (IQR)1.4431126 × 1010

Descriptive statistics

Standard deviation2.814130934 × 1010
Coefficient of variation (CV)2.064047627
Kurtosis16.40918809
Mean1.363404069 × 1010
Median Absolute Deviation (MAD)1345849000
Skewness3.602860666
Sum1.281599825 × 1012
Variance7.919332911 × 1020
MonotonicityNot monotonic
2021-10-31T10:35:16.193202image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
200000002
 
1.7%
180000002
 
1.7%
500000002
 
1.7%
300000002
 
1.7%
1000000002
 
1.7%
5000001
 
0.8%
28893940001
 
0.8%
1.221183 × 10111
 
0.8%
6.0728103 × 10101
 
0.8%
51614570001
 
0.8%
Other values (79)79
66.4%
(Missing)25
 
21.0%
ValueCountFrequency (%)
5000001
0.8%
30000001
0.8%
75000001
0.8%
80000001
0.8%
100000001
0.8%
180000002
1.7%
200000002
1.7%
210000001
0.8%
220000001
0.8%
250000001
0.8%
ValueCountFrequency (%)
1.84793461 × 10111
0.8%
1.221183 × 10111
0.8%
8.5732579 × 10101
0.8%
8.4227635 × 10101
0.8%
6.0728103 × 10101
0.8%
5.9250181 × 10101
0.8%
5.2388364 × 10101
0.8%
5.0872148 × 10101
0.8%
4.5111315 × 10101
0.8%
4.3336598 × 10101
0.8%

TotalCost: Flood
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct69
Distinct (%)100.0%
Missing50
Missing (%)42.0%
Infinite0
Infinite (%)0.0%
Mean1.142336591 × 1010
Minimum230000
Maximum7.0757047 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2021-10-31T10:35:16.362031image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum230000
5-th percentile24400000
Q1556300000
median3801950000
Q31.793967 × 1010
95-th percentile4.70542054 × 1010
Maximum7.0757047 × 1010
Range7.0756817 × 1010
Interquartile range (IQR)1.738337 × 1010

Descriptive statistics

Standard deviation1.586724413 × 1010
Coefficient of variation (CV)1.389016534
Kurtosis3.18130863
Mean1.142336591 × 1010
Median Absolute Deviation (MAD)3721950000
Skewness1.828683856
Sum7.88212248 × 1011
Variance2.517694362 × 1020
MonotonicityNot monotonic
2021-10-31T10:35:16.520687image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
78626630001
 
0.8%
3.2867943 × 10101
 
0.8%
500000001
 
0.8%
4380000001
 
0.8%
310000001
 
0.8%
38961000001
 
0.8%
37567280001
 
0.8%
11362430001
 
0.8%
38019500001
 
0.8%
7375990001
 
0.8%
Other values (59)59
49.6%
(Missing)50
42.0%
ValueCountFrequency (%)
2300001
0.8%
150000001
0.8%
190000001
0.8%
200000001
0.8%
310000001
0.8%
500000001
0.8%
800000001
0.8%
1000000001
0.8%
1882830001
0.8%
3000000001
0.8%
ValueCountFrequency (%)
7.0757047 × 10101
0.8%
5.738235 × 10101
0.8%
5.4782566 × 10101
0.8%
4.9137575 × 10101
0.8%
4.3929151 × 10101
0.8%
3.6240242 × 10101
0.8%
3.2867943 × 10101
0.8%
2.8497 × 10101
0.8%
2.7555794 × 10101
0.8%
2.6825511 × 10101
0.8%

TotalCost: Landslide
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct33
Distinct (%)97.1%
Missing85
Missing (%)71.4%
Infinite0
Infinite (%)0.0%
Mean308392176.5
Minimum27000
Maximum1277078000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2021-10-31T10:35:16.670741image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum27000
5-th percentile3138100
Q120000000
median66450000
Q3584850000
95-th percentile975598500
Maximum1277078000
Range1277051000
Interquartile range (IQR)564850000

Descriptive statistics

Standard deviation377620010
Coefficient of variation (CV)1.224479863
Kurtosis-0.1628396751
Mean308392176.5
Median Absolute Deviation (MAD)63467000
Skewness1.051801736
Sum1.0485334 × 1010
Variance1.42596872 × 1017
MonotonicityNot monotonic
2021-10-31T10:35:16.819726image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
200000002
 
1.7%
6258000001
 
0.8%
24660001
 
0.8%
2990000001
 
0.8%
63000001
 
0.8%
12770780001
 
0.8%
7250000001
 
0.8%
64000001
 
0.8%
1000000001
 
0.8%
7491000001
 
0.8%
Other values (23)23
 
19.3%
(Missing)85
71.4%
ValueCountFrequency (%)
270001
0.8%
24660001
0.8%
35000001
0.8%
63000001
0.8%
64000001
0.8%
80000001
0.8%
110000001
0.8%
163000001
0.8%
200000002
1.7%
217000001
0.8%
ValueCountFrequency (%)
12770780001
0.8%
9888000001
0.8%
9684900001
0.8%
9187000001
0.8%
8780360001
0.8%
7491000001
0.8%
7250000001
0.8%
7000000001
0.8%
6258000001
0.8%
4620000001
0.8%

TotalCost: Wildfire
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct41
Distinct (%)93.2%
Missing75
Missing (%)63.0%
Infinite0
Infinite (%)0.0%
Mean2005324432
Minimum1000000
Maximum2.2745 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2021-10-31T10:35:16.957129image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1000000
5-th percentile2150000
Q197500000
median735325000
Q32567484750
95-th percentile6258200000
Maximum2.2745 × 1010
Range2.2744 × 1010
Interquartile range (IQR)2469984750

Descriptive statistics

Standard deviation3757417008
Coefficient of variation (CV)1.873720256
Kurtosis21.9435561
Mean2005324432
Median Absolute Deviation (MAD)723780000
Skewness4.229334775
Sum8.8234275 × 1010
Variance1.411818257 × 1019
MonotonicityNot monotonic
2021-10-31T10:35:17.094232image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
10000000002
 
1.7%
10000002
 
1.7%
1000000002
 
1.7%
300000001
 
0.8%
4212000001
 
0.8%
30000001
 
0.8%
60950000001
 
0.8%
38500000001
 
0.8%
83092000001
 
0.8%
17213000001
 
0.8%
Other values (31)31
26.1%
(Missing)75
63.0%
ValueCountFrequency (%)
10000002
1.7%
20000001
0.8%
30000001
0.8%
80000001
0.8%
150900001
0.8%
247600001
0.8%
259750001
0.8%
300000001
0.8%
685000001
0.8%
900000001
0.8%
ValueCountFrequency (%)
2.2745 × 10101
0.8%
83092000001
0.8%
62870000001
0.8%
60950000001
0.8%
45974540001
0.8%
42832000001
0.8%
38500000001
0.8%
34398200001
0.8%
31370000001
0.8%
26162080001
0.8%

Interactions

2021-10-31T10:34:48.636505image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-31T10:34:48.778978image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-31T10:34:48.871060image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-31T10:34:48.966168image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-31T10:34:49.048797image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-31T10:34:49.145071image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-31T10:34:49.232467image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-31T10:34:49.322766image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-31T10:34:49.411820image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-31T10:34:49.513075image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-31T10:34:49.600898image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-31T10:34:49.693667image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-31T10:34:49.791865image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-31T10:34:49.893499image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-31T10:34:49.999307image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-31T10:34:50.087150image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-31T10:34:50.176061image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-31T10:34:50.259049image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-31T10:34:50.342382image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-31T10:34:50.425794image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-31T10:34:50.516273image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-31T10:34:50.596244image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-31T10:34:50.681091image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-31T10:34:50.765163image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-31T10:34:50.858773image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-31T10:34:50.959871image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-31T10:34:51.068646image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-31T10:34:51.180400image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-31T10:34:51.273990image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-31T10:34:51.382594image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-31T10:34:51.468873image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-31T10:34:51.571112image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-31T10:34:51.662730image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-31T10:34:51.765687image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-31T10:34:51.862347image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-31T10:34:51.959309image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-31T10:34:52.045180image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-31T10:34:52.143440image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-31T10:34:52.233151image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-31T10:34:52.329331image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-31T10:34:52.419708image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-31T10:34:52.508198image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-31T10:34:52.606058image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-31T10:34:52.709223image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-31T10:34:52.822292image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-31T10:34:52.920486image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-31T10:34:53.011174image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-31T10:34:53.096510image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-31T10:34:53.184169image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-31T10:34:53.273074image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-31T10:34:53.366260image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-31T10:34:53.449249image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-31T10:34:53.536187image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-31T10:34:53.628457image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-31T10:34:53.723871image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-31T10:34:53.811032image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-31T10:34:53.898312image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-31T10:34:53.993244image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-31T10:34:54.085527image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-31T10:34:54.187699image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-31T10:34:54.278884image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-31T10:34:54.381091image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-31T10:34:54.478246image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-31T10:34:54.581195image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-31T10:34:54.674606image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-31T10:34:54.773532image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-31T10:34:54.861850image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-31T10:34:54.956076image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-31T10:34:55.051098image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-31T10:34:55.165050image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-31T10:34:55.272585image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-31T10:34:55.365632image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-31T10:34:55.906642image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-31T10:34:56.027914image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-31T10:34:56.117367image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-31T10:34:56.211437image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-31T10:34:56.300463image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-31T10:34:56.380597image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-31T10:34:56.461018image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-31T10:34:56.541139image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-31T10:34:56.633030image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-31T10:34:56.719180image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-31T10:34:56.802477image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-31T10:34:56.887131image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-31T10:34:56.980502image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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Correlations

2021-10-31T10:35:17.239531image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-10-31T10:35:17.500846image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-10-31T10:35:17.741911image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-10-31T10:35:17.973733image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-10-31T10:35:11.677026image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-10-31T10:35:12.015964image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-10-31T10:35:12.310323image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-10-31T10:35:12.637008image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

YearAll natural disastersDroughtExtreme temperatureExtreme weatherFloodLandslideWildfireTotalCost: All natural disastersTotalCost: DroughtTotalCost: Extreme temperatureTotalCost: Extreme weatherTotalCost: FloodTotalCost: LandslideTotalCost: Wildfire
019005.02.0NaN1.01.0NaNNaN30000000.0NaNNaN30000000.0NaNNaNNaN
119012.0NaNNaNNaNNaNNaNNaN0.0NaNNaNNaNNaNNaNNaN
219029.0NaNNaN1.0NaNNaNNaN0.0NaNNaNNaNNaNNaNNaN
319038.01.0NaN2.02.0NaNNaN480000000.0NaNNaNNaN480000000.0NaNNaN
419042.0NaNNaN1.0NaNNaNNaN0.0NaNNaNNaNNaNNaNNaN
519054.0NaNNaN1.0NaNNaNNaN0.0NaNNaNNaNNaNNaNNaN
6190617.01.0NaN3.02.0NaNNaN650750000.0NaNNaN20000000.0NaNNaNNaN
719075.0NaNNaNNaNNaNNaNNaN30000000.0NaNNaNNaNNaNNaNNaN
819084.0NaNNaNNaNNaNNaNNaN116000000.0NaNNaNNaNNaNNaNNaN
9190911.0NaNNaN5.01.01.0NaN0.0NaNNaNNaNNaNNaNNaN

Last rows

YearAll natural disastersDroughtExtreme temperatureExtreme weatherFloodLandslideWildfireTotalCost: All natural disastersTotalCost: DroughtTotalCost: Extreme temperatureTotalCost: Extreme weatherTotalCost: FloodTotalCost: LandslideTotalCost: Wildfire
1092009344.018.025.087.0151.029.09.04.677639e+103.628700e+091.162000e+092.613466e+108.003878e+092.990000e+081.515000e+09
1102010393.017.029.094.0184.032.07.01.321941e+113.884700e+094.000000e+082.812408e+104.913758e+101.277078e+092.070000e+09
1112011334.017.016.084.0156.017.08.03.640932e+118.142000e+097.811230e+085.087215e+107.075705e+10NaN3.137000e+09
1122012346.021.051.090.0136.013.06.01.566922e+112.548000e+101.528010e+088.573258e+102.579054e+10NaN1.000000e+09
1132013332.09.014.0105.0149.011.010.01.194842e+111.087000e+091.000000e+095.238836e+105.478257e+10NaN1.072400e+09
1142014320.018.017.099.0135.015.04.09.776931e+101.100500e+102.518000e+094.011407e+103.624024e+102.730000e+082.590000e+08
1152015380.028.012.0118.0160.020.012.08.412014e+101.981240e+109.400000e+073.305177e+102.108630e+108.000000e+063.439820e+09
1162016325.015.012.084.0161.013.010.01.477812e+113.554000e+091.727000e+094.511132e+105.738235e+107.250000e+086.287000e+09
1172017276.07.011.085.0114.025.013.01.441086e+112.422000e+090.000000e+001.221183e+111.577868e+106.300000e+061.019000e+09
1182018282.013.025.084.0109.013.010.01.077716e+115.253669e+090.000000e+005.925018e+101.743615e+108.780360e+082.274500e+10